HIV/AIDS vs Other Causes Death Rate in South Africa (1990–2019)

CODAfrica <- read_excel("../Cause of Death AFRICA.xlsx")


if (!all(c("Entity", "Year") %in% names(CODAfrica))) {
  stop("Expected columns 'Entity' and 'Year' not found in Cause of Death AFRICA.xlsx")
}

SouthAfrica <- CODAfrica %>% filter(Entity == "South Africa")

SouthAfrica_long_format <- SouthAfrica %>%
  pivot_longer(cols = -c(Entity, Code, Year), names_to = "Cause", values_to = "Fatalities")


others <- SouthAfrica_long_format %>%
  filter(Cause != "HIV/AIDS fatalities") %>%
  group_by(Year, Cause) %>%
  summarise(Fatalities = sum(Fatalities, na.rm = TRUE), .groups = "drop") %>%
  mutate(Group = "Other")

hiv <- SouthAfrica_long_format %>%
  filter(Cause == "HIV/AIDS fatalities") %>%
  group_by(Year) %>%
  summarise(Fatalities = sum(Fatalities, na.rm = TRUE), .groups = "drop") %>%
  mutate(Group = "HIV")


p <- ggplot() +
  geom_line(data = others, aes(x = Year, y = Fatalities, group = Cause, color = Group),
            size = 0.7, alpha = 0.7) +
  geom_line(data = hiv, aes(x = Year, y = Fatalities, color = Group),
            size = 1.2) +
  geom_point(data = hiv, aes(x = Year, y = Fatalities, color = Group),
             size = 1.7) +
  scale_color_manual(values = c("HIV" = "red", "Other" = "grey70")) +
  labs(title = "HIV/AIDS vs Other Causes in South Africa (1990–2019)",
       x = "Year", y = "Number of Fatalities", color = NULL) +
  theme_minimal() +
  scale_y_continuous(labels = label_comma()) +
  theme(legend.position = "right") +
  guides(color = guide_legend(override.aes = list(size = c(1.2, 0.8), alpha = c(1, 0.8))))

ggplotly(p)

The plot illustrates the stark contrast between fatalities due to HIV/AIDS and other causes of death in South Africa from 1990 to 2019.